Does Traversal Order Matter? A Systematic Study of Tree Traversal Methods in Transformer Grammars
arXiv:2606.16836v1 Announce Type: new Abstract: Transformer Grammars (TGs) enhance language modeling by incorporating syntactic tree structures. Despite the potentially significant impact on model performance of how syntactic trees are linearized in TGs, existing studies rely solely on Depth-First Traversal (DFT) for linearization. In this paper, we expand the traversal design space by exploring Breadth-First Traversal (BFT) and a novel hybrid traversal strategy, Production-Rule Traversal (PRT), which combines the structural lookahead of BFT with the early lexical generation of DFT. We integra
The continuous evolution of Transformer architectures necessitates deeper investigation into underlying computational mechanisms, especially as models scale in complexity and application.
Optimizing how syntactic information is fed into Transformer models can lead to significant improvements in language understanding and generation, impacting a wide range of AI applications.
This research introduces new methods for linearizing tree structures in Transformer Grammars, potentially offering more efficient and effective ways to integrate linguistic syntax into large language models.
- · AI researchers
- · NLP developers
- · Companies building advanced LLMs
- · Prior methods relying solely on DFT without optimization
Improved performance and efficiency of Transformer-based language models.
Faster development and deployment of more nuanced and robust AI applications across various industries.
Potentially lower computational costs for training and inference, democratizing access to powerful AI models.
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